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Cutting-edge cancer therapy involves producing individualized medicine for many patients at the same time. Within this process, most steps can be completed for a certain number of patients simultaneously. Using these resources efficiently may significantly reduce waiting times for the patients and is therefore crucial for saving human lives. However, this involves solving a complex scheduling problem, which can mathematically be modeled as a proportionate flow shop of batching machines (PFB). In this thesis we investigate exact and approximate algorithms for tackling many variants of this problem. Related mathematical models have been studied before in the context of semiconductor manufacturing.

Optimal control of partial differential equations is an important task in applied mathematics where it is used in order to optimize, for example, industrial or medical processes. In this thesis we investigate an optimal control problem with tracking type cost functional for the Cattaneo equation with distributed control, that is, \(\tau y_{tt} + y_t - \Delta y = u\). Our focus is on the theoretical and numerical analysis of the limit process \(\tau \to 0\) where we prove the convergence of solutions of the Cattaneo equation to solutions of the heat equation.
We start by deriving both the Cattaneo and the classical heat equation as well as introducing our notation and some functional analytic background. Afterwards, we prove the well-posedness of the Cattaneo equation for homogeneous Dirichlet boundary conditions, that is, we show the existence and uniqueness of a weak solution together with its continuous dependence on the data. We need this in the following, where we investigate the optimal control problem for the Cattaneo equation: We show the existence and uniqueness of a global minimizer for an optimal control problem with tracking type cost functional and the Cattaneo equation as a constraint. Subsequently, we do an asymptotic analysis for \(\tau \to 0\) for both the forward equation and the aforementioned optimal control problem and show that the solutions of these problems for the Cattaneo equation converge strongly to the ones for the heat equation. Finally, we investigate these problems numerically, where we examine the different behaviour of the models and also consider the limit \(\tau \to 0\), suggesting a linear convergence rate.

Recently, a new Quicksort variant due to Yaroslavskiy was chosen as standard sorting
method for Oracle's Java 7 runtime library. The decision for the change was based on
empirical studies showing that on average, the new algorithm is faster than the formerly
used classic Quicksort. Surprisingly, the improvement was achieved by using a dual pivot
approach — an idea that was considered not promising by several theoretical studies in the
past. In this thesis, I try to find the reason for this unexpected success.
My focus is on the precise and detailed average case analysis, aiming at the flavor of
Knuth's series “The Art of Computer Programming”. In particular, I go beyond abstract
measures like counting key comparisons, and try to understand the efficiency of the
algorithms at different levels of abstraction. Whenever possible, precise expected values are
preferred to asymptotic approximations. This rigor ensures that (a) the sorting methods
discussed here are actually usable in practice and (b) that the analysis results contribute to
a sound comparison of the Quicksort variants.

Data usage control is a concept that extends access control to also protect data after it
has been released. Usage control enforcement relies on available information about the
distribution of data in the monitored system. In this thesis we introduce an information
flow tracking approach for JavaScript in order to enable usage control for dynamic content
in web browsers. The proposed model is implemented as a prototype in the JavaScript
engine V8 of the Chromium browser to evaluate the feasibility of the chosen approach.

In the present master’s thesis we investigate the connection between derivations and
homogeneities of complete analytic algebras. We prove a theorem, which describes a specific set of generators
for the module of derivations of an analytic algebra, which map the maximal ideal of R into itself. It turns out, that this set has a structure similar to a Cartan subalgebra and contains
information regarding multi-homogeneity. In order to prove
this theorem, we extend the notion of grading by Scheja and Wiebe to projective systems and state the connection between multi-gradings and pairwise
commuting diagonalizable derivations. We prove a theorem similar to Cartan’s Conjugacy Theorem in the setup of infinite-dimensional Lie algebras, which arise as projective limits of finite-dimensional Lie algebras. Using this result, we can show that the structure of the aforementioned set of generators is an intrinsic property of the analytic algebra. At the end we state an algorithm, which is theoretically able to compute the maximal multi-homogeneity of a complete analytic algebra.

Music Information Retrieval (MIR) is an interdisciplinary research area that has the goal to improve the way music is accessible through information systems. One important part of MIR is the research for algorithms to extract meaningful information (called feature data) from music audio signals. Feature data can for example be used for content based genre classification of music pieces. This masters thesis contributes in three ways to the current state of the art: • First, an overview of many of the features that are being used in MIR applications is given. These methods – called “descriptors” or “features” in this thesis – are discussed in depth, giving a literature review and for most of them illustrations. • Second, a large part of the described features are implemented in a uniform framework, called T-Toolbox which is programmed in the Matlab environment. It also allows to do classification experiments and descriptor visualisation. For classification, an interface to the machine-learning environment WEKA is provided. • Third, preliminary evaluations are done investigating how well these methods are suited for automatically classifying music according to categorizations such as genre, mood, and perceived complexity. This evaluation is done using the descriptors implemented in the T-Toolbox, and several state-of-the-art machine learning algorithms. It turns out that – in the experimental setup of this thesis – the treated descriptors are not capable to reliably discriminate between the classes of most examined categorizations; but there is an indication that these results could be improved by developing more elaborate techniques.

Nowadays, vehicle control systems such as anti-lock braking systems, electronic stability control, and cruise control systems yield many advantages. The electronic control units that are deployed in this specific application domain are embedded systems that are integrated in larger systems to achieve predefined applications. Embedded systems consist of embedded hardware and a large software part. Model-based development for embedded systems offers significant software-development benefits that are pointed out in this thesis. The vehicle control system Adaptive Cruise Control is developed in this thesis using a model-based software development process for embedded systems. As a modern industrial design tool that is prevalent in this domain, simulink,is used for modeling the environment, the system behavior, for determining controller parameters, and for simulation purposes. Using an appropriate toolchain, the embedded code is automatically generated. The adaptive cruise control system could be successfully implemented and tested within this short timespan using a waterfall model without increments. The vehicle plant and important filters are fully deduced in detail. Therefore, the design of further vehicle control systems needs less effort for development and precise simulation.

In its rather short history robotic research has come a long way in the half century since it started to exist as a noticeable scientic eld. Due to its roots in engineering, computer science, mathematics, and several other 'classical' scientic branches,a grand diversity of methodologies and approaches existed from the very beginning. Hence, the researchers in this eld are in particular used to adopting ideas that originate in other elds. As a fairly logical consequence of this, scientists tended to biology during the 1970s in order to nd approaches that are ideally adapted to the conditions of our natural environment. Doing so allows for introducing principles to robotics that have already shown their great potential by prevailing in a tough evolutionary selection process for millions of years. The variety of these approaches spans from efficient locomotion, to sensor processing methodologies and all the way to control architectures. Thus, the full spectrum of challenges for autonomous interaction with the surroundings while pursuing a task can be covered by such means. A feature that has proven to be amongst the most challenging to recreate is the human ability of biped locomotion. This is mainly caused by the fact that walking,running and so on are highly complex processes involving the need for energy efficient actuation, sophisticated control architectures and algorithms, and an elaborate mechanical design while at the same time posting restrictions concerning stability and weight. However, it is of special interest since our environment is favoring this specic kind of locomotion and thus promises to open up an enormous potential if mastered. More than the mere scientic interest, it is the fascination of understanding and recreating parts of oneself that drives the ongoing eorts in this area of research. The fact that this is not at all an easy task to tackle is not only caused by the highly dynamical processes but also has its roots in the challenging design process. That is because it cannot be limited to just one aspect like e.g. the control architecture, actuation, sensors, or mechanical design alone. Each aspect has to be incorporated into a sound general concept in order to allow for a successful outcome in the end. Since control is in this context inseparably coupled with the mechanics of the system, both has to be dealt with here.

Ever since Mark Weiser’s vision of Ubiquitous Computing the importance of context has increased in the computer science domain. Future Ambient Intelligent Environments will assist humans in their everyday activities, even without them being constantly aware of it. Objects in such environments will have small computers embedded into them which have the ability to predict human needs from the current context and adapt their behavior accordingly. This vision equally applies to future production environments. In modern factories workers and technical staff members are confronted with a multitude of devices from various manufacturers, all with different user interfaces, interaction concepts and degrees of complexity. Production processes are highly dynamic, whole modules can be exchanged or restructured. Both factors force users to continuously change their mental model of the environment. This complicates their workflows and leads to avoidable user errors or slips in judgement. In an Ambient Intelligent Production Environment these challenges have to be approached. The SmartMote is a universal control device for ambient intelligent production environments like the SmartFactoryKL. It copes with the problems mentioned above by integrating all the user interfaces into a single, holistic and mobile device. Following an automated Model-Based User Interface Development (MBUID) process it generates a fully functional graphical user interface from an abstract task-based description of the environment during run-time. This work introduces an approach to integrating context, namely the user’s location, as an adaptation basis into the MBUID process. A Context Model is specified, which stores location information in a formal and precise way. Connected sensors continuously update the model with new values. The model is complemented by a reasoning component which uses an extensible set of rules. These rules are used to derive more abstract context information from basic sensor data and for providing this information to the MBUID process. The feasibility of the approach is shown by using the example of Interaction Zones, which let developers describe different task models depending on the user’s location. Using the context model to determine when a user enters or leaves a zone, the generator can adapt the graphical user interface accordingly. Context-awareness and the potential to adapt to the current context of use are key requirements of applications in ambient intelligent environments. The approach presented here provides a clear procedure and extension scheme for the consideration of additional context types. As context has significant influence on the overall User Experience, this results not only in a better usefulness, but also in an improved usability of the SmartMote.

Buses not arriving on time and then arriving all at once - this phenomenon is known from
busy bus routes and is called bus bunching.
This thesis combines the well studied but so far separate areas of bus-bunching prediction
and dynamic holding strategies, which allow to modulate buses’ dwell times at stops to
eliminate bus bunching. We look at real data of the Dublin Bus route 46A and present
a headway-based predictive-control framework considering all components like data
acquisition, prediction and control strategies. We formulate time headways as time series
and compare several prediction methods for those. Furthermore we present an analytical
model of an artificial bus route and discuss stability properties and dynamic holding
strategies using both data available at the time and predicted headway data. In a numerical
simulation we illustrate the advantages of the presented predictive-control framework
compared to the classical approaches which only use directly available data.